Java · Spring Boot · Distributed Systems
Production-grade Java and Spring Boot patterns: authentication boundaries, gateway-level rate limiting, event streaming, and the backend decisions that hold up under real load.
Featured Articles
Why latency spikes, timeouts, and unused capacity are signs your thread pools need redesign. Workload-aligned architecture patterns for Spring Boot and Java.
How we implemented distributed rate limiting in Spring Cloud Gateway using Redis shared state, fallback resilience, and layered edge protection in production.
Gateway-centric, distributed, or internal tokens? A practical guide to JWT trust boundaries with real-world tradeoffs across four production patterns.
Why JWT validation is only the beginning, and how small security decisions become expensive architectural problems at scale.
How we kept PostgreSQL synchronized with OpenSearch using Kafka Connect, with reconciliation to ensure business confidence in the data.
Reliable backend systems depend on explicit boundaries for identity, traffic, data, and asynchronous work. Authentication design should define which component validates a token, what each service trusts, and how internal calls are protected. Rate limiting needs a clear policy and shared state when limits must apply across multiple application instances. These decisions affect availability and security as much as the framework or language used to implement them.
Our backend engineering articles cover Java and Spring patterns for these problems, including gateway-level JWT validation, Redis-backed rate limiting, thread-pool isolation, and Kafka Connect data synchronization. They discuss tradeoffs and failure handling so engineering teams can evaluate an approach in the context of their own workloads. Explore the microservices architecture articles for related guidance on service boundaries and distributed-system design.